Following is the description of a course in Bioinformatics at Department
of informatics, University of Bergen.
For a brief description of our activity in Bioinformatic, please
use WWW with URL-address http://www.ii.uib.no/~linda/bio/top.eng.html
The description of the course will be found by choosing "education".
Ingvar Eidhammer
COURSE IN BIOINFORMATICS (Computational Molecular Biology)
At the University of Bergen, Department of informatics, this autumn we will
offer a course in Bioinformatics (I282) (Computational Molecular Biology).
The course will be given with assistance from the Laboratory for Biotechnology.
Ingvar Eidhammer is responsible for the course.
OBJECTIVES
I282 is a graduate course primarily for students studying informatics.
The aim is to give the students insight into problems which the molecular
biologists are working with, and how informatics can be used in trying to
solve some of these problems. No knowledge in biology is required, and a brief
introduction to molecular biology will be given at the beginning of the course.
We assume that the students have an advanced course in algorithms
The different problems will be explained from a biological and informatical
viewpoint, and the methods and techniques will be explained algorithmically.
In addition we will emphasize on the theoretical and the biological foundation
for the different methods. Therefore, statistics and biological significance will
be stressed. The students will be given practical exercises in use of different
existing tools.
There will be two lectures per week in thirteen weeks, one lecture is of 2 x 45
minutes.
In addition there will be practical and theoretical exercises.
THE CONTENTS
A brief outline of the course contents is given below. The numbers in parenthesis
are the planned number of lectures for each subject.
I. Introduction to molecular biology (4 lectures)
Cell, Molecule, Gene, Chromosome, DNA, RNA, Protein, Relationship DNA-RNA-protein,
Protein structures, Protein functions, Bio-chemical properties of amino acids,
Motif, Domain, Protein families, Evolution, Similarity, Homology.
II. General search methods (2 lectures)
Means-ends analysis, Problem reduction, Goal tree, DepthFSearch, BreadthFSearch,
BestFSearch, Optimal search, Branch and bound, Dynamic programming
principle, Minimax-procedure.
III. Statistical preliminaries (1 lecture)
Basic theory in probability and statistics, Sampling and sampling distributions,
Estimation, Testing hypotheses.
IV. Scoring systems (2 lectures)
PAM-Matrices (Dayhoff), BLOSUM (Henikoff and Henikoff), Scoring systems
based on amino acid classifications.
V. String searching (4 lectures)
Exact string matching, String distances, Approximate string matching (k
mismatches, k differences), Suffix trees.
VI. Construction of phylogenetic trees (2 lectures)
Methods based on pairwise distances (UPGMA), Methods based on minimum evolution
(NJ).
VII. Search in biological databases (5 lectures)
Statistical significance, Dot matrix methods, Dynamic programming (MPSRCH), FASTA,
BLAST, Search with profiles.
VIII. Pattern (Motiv) discovery in sets of biosequences (3 lectures)
Pattern description languages, Top-down methods (based on multiple alignment),
Bottom-up methods (based on enumerating substrings), Combined methods.
IX. Global multiple alignment (2 lectures)
Cost (measure) of multiple alignment, Dynamic programming, Progressive alignment
(CLUSTAL), Via local multiple alignment (VIII).
X. Protein secondary structure prediction (1 lecture)
Homology modelling, Inverse folding, Direct prediction methods.
LITERATURE
Since no book cover all the topics, the lectures will be based on
different sources. For the topics in points IV, VII, VIII and IX, we
develop our own literature (in Norwegian).